In data migration/integration projects, data is moved from files at a source location to files at a target location. Typically, the data to be moved is stored in tables of some kind, organized by table and by column within the data. This data may need to be integrated with existing data at the target location, and therefore transferring the source files in full and maintaining their existing table and column names, etc. is not an option. Rather, it is necessary to determine which tables and columns within those tables at the target location correspond to given tables and table columns of the source data. This process of mapping source data to target data is referred to as data mapping.
Existing data mapping applications lack standardization, collaboration, versioning, traceability, impact analysis, management visibility, auditability and programmatic control. The data integration industry creates data mappings manually, using manpower and spreadsheets and manually typing and copy/pasting tables and columns and typing transformation rules into spreadsheets. The manual mapping process lacks control, auditability and visibility into the mappings, is non-standard, time consuming, error-prone, and costly and makes management of the mappings and tracking of changes very difficult or impossible. Inaccurate and incomplete mapping rules negatively impact data integration projects by driving up costs and delivery timeframes. Organizations managing large amounts of data and data migration projects frequently end up with hundreds or thousands of manually-created data mapping spreadsheets and/or document files, each tailored to a given data migration project and lacking any centralization or organization. Building mapping specifications for the ETL process is manual.
Needs exist for improved data mapping applications.